Correlative weighted stacking for seismic data in the wavelet domain

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Abstract

Horizontal stacking plays a crucial role for modern seismic data processing, for it not only compresses random noise and multiple reflections, but also provides a foundational data for subsequent migration and inversion. However, a number of examples showed that random noise in adjacent traces exhibits correlation and coherence. The average stacking and weighted stacking based on the conventional correlative function all result in false events, which are caused by noise. Wavelet transform and high order statistics are very useful methods for modern signal processing. The multiresolution analysis in wavelet theory can decompose signal on difference scales, and high order correlative function can inhibit correlative noise, for which the conventional correlative function is of no use. Based on the theory of wavelet transform and high order statistics, high order correlative weighted stacking (HOCWS) technique is presented in this paper. Its essence is to stack common midpoint gathers after the normal moveout correction by weight that is calculated through high order correlative statistics in the wavelet domain. Synthetic examples demonstrate its advantages in improving the signal to noise (S/N) ration and compressing the correlative random noise.

Additional Publication Details

Publication type:

Conference Paper

Publication Subtype:

Conference Paper

Title:

Correlative weighted stacking for seismic data in the wavelet domain

ISBN:

1880132974

Year Published:

2004

Language:

English

First page:

161

Last page:

165

Number of Pages:

5

Conference Title:

Progress in Environmental and Engineering Geophysics: Proceedings of the International Conference on Environmental and Engineering Geophysics, ICEEG 2004